Embedded locality discriminant GPLVM for dimensionality reduction

Han, B; Zhang, L; Gao, X; Zhao, X; Tao, D

Embedded locality discriminant GPLVM for dimensionality reduction

Han, B Zhang, L Gao, X Zhao, X Tao, D

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2016, 2016-October pp. 2431 - 2438
Issue Date:: 2016-10-31

Closed Access

	Filename	Description	Size
	07727501.pdf	Published version	522.85 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Han, B	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Zhao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2016-10-31	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2016, 2016-October pp. 2431 - 2438	en_US
dc.identifier.isbn	9781509006199	en_US
dc.identifier.uri	http://hdl.handle.net/10453/102349
dc.description.abstract	© 2016 IEEE. The Gaussian process latent variable model (GPLVM) had been proved to be good at discovering low-dimension manifold from nonlinear high-dimensional data for small training sets. However, for labeled data, GPLVM cannot achieve a better result because it doesn't use the label information. It turned out to be an effective strategy to employ a discriminative prior over the latent space according to the label information. Existing methods for discriminative GPLVM roughly utilized label information data and ignored the natural structure of the data. In this paper, we embedded the locality discriminative information into the GPLVM which not only preserved the locality of data, but also use the label information of samples. Compared to the discriminative GPLVM, our Embedded Locality Discriminant GPLVM (ELD-GPLVM) introduces a local strategy to extract the discriminative information in local region. Experimental results on UCI datasets show that the proposed algorithm has a good performance on no matter small-scale data sets or a larger-scale dataset.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2016.7727501	en_US
dc.title	Embedded locality discriminant GPLVM for dimensionality reduction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-October	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2016-October	en_US

Abstract:

© 2016 IEEE. The Gaussian process latent variable model (GPLVM) had been proved to be good at discovering low-dimension manifold from nonlinear high-dimensional data for small training sets. However, for labeled data, GPLVM cannot achieve a better result because it doesn't use the label information. It turned out to be an effective strategy to employ a discriminative prior over the latent space according to the label information. Existing methods for discriminative GPLVM roughly utilized label information data and ignored the natural structure of the data. In this paper, we embedded the locality discriminative information into the GPLVM which not only preserved the locality of data, but also use the label information of samples. Compared to the discriminative GPLVM, our Embedded Locality Discriminant GPLVM (ELD-GPLVM) introduces a local strategy to extract the discriminative information in local region. Experimental results on UCI datasets show that the proposed algorithm has a good performance on no matter small-scale data sets or a larger-scale dataset.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/102349